\section{application}%taken from attempt to publich LAILAPS in specical
% issue database journal PCP
user statistics (year 0 = 2011)

\# site visits
Yr 0	226
Yr 1	460
Yr 2	802
Yr 3 	971

\# unique users
We don't have the ip address information before 2014.
Yr 0 n/a
Yr 1 n/a
Yr 2 n/a
Yr 3 101

\# indexed records in knowledge bases
Yr 0 12,520,000
Yr 1 24,530,000
Yr 2 50,676,110
Yr 3 56,354,620

\# linked records in genome databases

Yr 0 0
Yr 1 7,183,702
Yr 2 40,252,876
Yr 3 55,017,222


Abstract (max. 250 words):
In the last years more and more plant genomes were sequenced, genes were
predicted and their functions annotated. The association of genes with
phenotypic traits is in the focus of current plant research. Timeliness this
information is widely scattered over different databases. In order to get a
comprehensive view from genes to traits, information retrieval (IR) has become
an encompassing bioinformatics discipline to extract systematized knowledge from
complex, heterogeneous and distributed databases. Here we present the
information retrieval system LAILAPS (http://lailaps.ipk-gatersleben.de) as a
platform to interpret plant genomic data in the context of phenotypes for a
detailed forward genetic research. It comprises around 65 million indexed
documents over thirteen major life science databases and around 80 million links
to plant genomic resources.
LAILAPS allows fuzzy querying for candidate genes to a specific trait over a
loosely integrated system of indexed and interlinked genome databases. Synonym
expansion, spelling correction and an evidence based annotation system enable a
time efficient and comprehensive information retrieval. The results get
relevance ranked by an artificial neural network that incorporated user
feedback. The relevance prediction is based on discriminating features that were
determined by domain experts and evaluated for candidate gene predictions.
To demonstrate the usage of LAILAPS we present a showcase based on a forward
genetic approach.

Keywords:
Plant Genomics Resources, Integrative Search Engine, Information Retrieval;
Functional Gene Annotation; Traits;   Introduction (~635/500):
Crop plants are important for the human nutrition. The worldwide food production
faces problems like climate change or limited water resources. However, it is
necessary to increase the food production by 70% to 100% by 2050 to keep pace
with the predicted population growth and changes in diet. To support breeders to
select elite cultivars to avoid crop losses and reduction in quality, relevant
genes which may be part of a causal chain for an improvement agronomic
properties, a response to abiotic and abiotic stress need to be studied and
analyzed. The progress in molecular biology, ranging from experimental data
acquisition on individual genes and proteins, over post genomics technologies,
such as RNA-seq, phenotyping, proteomics, systems biology and integrative
bioinformatics aims to capture the big picture of entire biological systems
(Kitano 2002). The wave of new technologies forces data generation at
unprecedented scales (Schadt et al. 2010). As a consequence of this revolution,
the number of plant genomic sequence data and thereby the number of plant genes
and their functional annotations increasing steadily. As of September 2014 the
Uniprot protein knowledge base reached a number of 82,673,135 (UniProt Release
Statistics 2014), NCBI Genbank plant division provides access to 24,385,026
sequences and PubMed comprises over 24 million citations for biomedical
literature from MEDLINE, life science journals, and online books. Overall, The
number of public available databases passed recently the high water mark of
1,552 (Fernández-Suárez et al. 2014).
Despite the paramount information potential of these public database resources
the concrete search for candidate genes and relevant genomic data is a time
consuming and sophisticated task (Divoli et al. 2008). This data deluge must now
be harnessed and exploited. Over the past years, information processing
techniques evolved from library research and individual data archives to web
based systems using intercontinental high speed network links for an ad-hoc data
exchange, cloud computing and distributed databases. This continuous and ongoing
shift is attended by the use of database information systems which are applied
to the management of increasingly complex data structures and voluminous content
(Stein 2010, Lange et al. 2014). The consequences of this development are new
requirements for information retrieval methods. Typically, life scientists and
bioinformaticians formulate their queries rather vaguely. This does not
necessarily happen due to inexperience or ignorance, but because their search is
often explorative with no clear idea of the expected answer. Vague queries
though pose a problem on current databases and information systems as these
queries cannot be semantically interpreted, without comprehensive semantic
document tagging or the use of controlled vocabulary. Much more specific
problems such as data distribution and isolation, structural heterogeneity, less
meta data, interfaces, query languages and deep (invisible) web are further
examples of the underlying challenges. Summarized, the major challenge is the
ranked extraction of knowledge from those complex and heterogeneous databases
that fulfill the personal information needs and requires suited information
retrieval (IR) methods.
Currently there is a increasing number of published information retrieval in
life sciences. Their application and features range from a simple text index for
in-house databases, general purpose open source IR frameworks (Apache Lucene,
BioMart) and comprehensive platform for an integrative database search (NCBI
GQuery) towards tailored life science IR systems (IntegromDB). A well-known and
quite frequently applied system for life science IR is Google. It works well for
general information but fails quickly in more specific information searches.
More dedicated life science search engines and information systems for gene
annotations are available. A popular resource for protein sequences and their
functional annotations is UniProt (UniProt Consortium 2014). Manually reviewed
sequences are stored in UniProtKB/Swiss-Prot whereas unreviewed sequences can be
found in UniProtKB/TrEMBL. A search toolbar allows a differentiated search and
results can be sorted by the user according various fields or using the UniProt
default score. Ensemble (Kersey et al. 2014) Plants stores genome information
for different plant species. Gene information gets visualized and sequence data
can be downloaded by the user. The search options are less comprehensive than in
UniProt. More general systems that integrate different data sources are EB-eye
(Valentin et al. 2010), DBGET Search (Fujibuchi et al. 1998), Gene Ontology
(Ashburner et al. 2000) and MIPS PlantsDB (Nussbaumer et al 2013). Moreover,
PubMed (PubMed Help 2014) comprises citation abstracts from life science
journals and is part of the search engine GQuery (NCBI 2005) from the National
Center for Biotechnology Information (NCBI). Here, the number of available
databases is quite impressive, but the approach to navigate through all
referenced systems separately is a time consuming and laborious task. In
addition many of these systems don’t provide sufficient methods to preprocess
users request and offer a better area of usability. IntegromeDB (Baitaluk et al.
2012) focus to apply state of the art IR technology to scan heterogeneous, multi
domain web resources and databases and compile comprehensive knowledge reports.
Literature study, daily work with public IR systems as well as experiences from
dozens research projects and own developed information systems motivated to
structure this in a catalog of minimal requirements for IR systems in the frame
of plant genome research. The most important aspects for an efficient and user
friendly IR environment life science sources can be summarized as:
1. IR technology o Timeliness o Data range o Ranking 2. User assistance o User
pertinence o Data card o Query assistance o Interactive result filtering o Link
to related data In the matter of technology, the integration of a non-replicated
set of miscellaneous data domains is important to offer a compact, comprehensive
information source. Next, IR should be trustworthy in term of facts and
timeliness. In particular, the data range plays a crucial role in the efficient
search. A large document breath delivers a wide range of cross domain
information. A big data deep with high structural degree and number of
attributes provides high information content. Result rankings and an interactive
result filtering helps to classify the importance of single documents.
Further aspects are the user assistance for query formulation and result
filtering. Query preprocessing should suggest corrections, semantic expansions
or point to similar queries by other user. Further essential criteria in respect
to user acceptance are persistence and integration into personalized tagged
bookmark list of query results (data cart) and its integration into downstream
processing workflows are essential for the user acceptance. Last but not least
is the link to related data. Examples for links sources are database cross
references in the indexed databases, predicted references among indexed
documents by text similarity and genome annotations.
Here we present the information retrieval system LAILAPS as an IR platform to
explore plant genomic data in a phenotype perspective to support a forward
genetic research. As a search engine for plant genomic data it reduce time in
front the screen and increase the specificity and comprehensiveness of search
results.
Results (~800/1500):
LAILAPS (http://lailaps.ipk-gatersleben.de) is a comprehensive information
retrieval platform for plant genomes. Its focus is to support a highly specific
study of phenotype-genotype associations. Rather than integrate a relevant
databases tightly, we assume that genomics replicates to a certain extend data
among each other. Furthermore, genomics resources build a network of cross
references. Its basis are functional genome annotations, e.g. by homology
search, sequence pattern search or manual curated annotation to literature
databases. To combine the potential of both, LAILAPS combines the advantages of
a materialized integration of less replicative major information hubs in plant
genomics, linked integration of specialized genome resources and an effective IR
technology. This approach avoids an elaborate storage and indexing of dozens of
databases but keep the information potential. As result from a questionnaire
among plant genome scientists, LAILAPS index 12 major repositories for genome
annotation and maintain references to 13 genome databases (see Table 1). As
compromise between timeliness and maintenance effort the text index of genome
annotation repositories is refreshed quarterly. In general, novel genome
annotations are published as result of community effort and mid-term projects.
Thus, will be updated the references to genome database on demand. The
particular update dates is available in the LAILAPS frontend.

Beside the covered information potential, an important factor for an IR system
is the data quality. Rather than flooding scientist screen with data, LAILAPS
apply ranking and filtering methods to predict most reasonable data item and
rank them according to their relevance in respect to the information needs of
user groups.  The implemented approach scores 11 properties of the database
entry that score relationships between the query and the picked data item as
well static features of the item itself. To compute from those scores relevance
an artificial neural network is used and trained specific for user groups. Using
an expert curated training set, the presented LAILAPS portal was training for
application in plant science. Furthermore, a feedback system enables the
inclusion of individual feedback to improve the prediction performance in
general or to link individual trained neural networks into user profiles.
Closely connected to a high specificity of IR is the aim to maximize the search
sensitivity. But not necessarily increasing the data range in depth and breadth
means a useful sensitivity increase. The computer aided query formulation
enables an IR system to interactively include the user expertise to explore such
data that would not matched by keyword queries using string matching algorithms.
Rather, LAILAPS suggest interactively query alternatives (Esch et al. 2014).
This is achieved by an enhanced real time spelling correction, synonym
expansion, stop word removal and the suggestion of related entries by document
similarity for each item in the query result. Furthermore, the guidance in
exploration of the query results to offer an after query hierarchical filter.
Here, LAILAPS apply concepts of faceted search by group results by database,
synonyms and linked genome resources and annotation evidence.
LAILAPS frontend reused establish design pattern for Web search engines. A text
box takes a keyword query, whereas possible search alternatives or spelling
corrections are suggested below each single word and the number of estimated
hits for the whole query is shown. The results are shown as relevance ordered
excerpt of the hits and for each evidence ordered list of cross references to
genome features. LAILAPS support either the navigation to the annotation or
genome is supported as well downloads of results for use as data cart.
Furthermore, user feedback is included by a rating system for each ranked hit in
form of an interactive rating system, which may be applied for a personalized
relevance ranking. Figure x show these and further major frontend components.
A possible request could cover the search of candidate genes which are involved
in flowering time or influence the circadian rhythm in barley. Knowledge about
these genes plays an important role for crop yield and environmental adaptation.
Ariyadasa et al. (2014) published a physical map with cloned barley genes
whereas one of the markers was Ppd-H1 known to be a regulator of photoperiodic
response. It is annotated on a whole-genome shotgun contig where the gene
MLOC\_81154.10 is located on. Searching for “flowering time in barley” brings
more than twice as many results in LAILAPS than “circadian clock in barley”
which is caused in the more frequent use of the term flowering time. But both
search results have in common that the known annotated gene is under the top 10
ranked documents including direct and indirect links into the search. One top
ranked document linking to this gene is the Pseudo-response regulator 1 a
protein which is related to the gene ontologies circadian rhythm and negative
regulation of gene expression among other terms.
The example demonstrates the strengths of an information system as LAILAPS it
is. (…)

Discussion (~450/500):
LAILAPS offers a new quality of bundled information retrieval in plant research.
It is designed to guide biologist with different expertise, research background
and application field to their information of interest.  It allows a width and
deep search over plant genome databases. Especially, the wide spectrums of
covered data domains as well as a deep drilldown of cross references and data
structures support a maximum use of the information potential from distributed
and heterogeneous plant genome resources. By the means of user pertinence aware
relevance ranking algorithm, feedback system and evidence filtered merging of
cross referenced data, LAILAPS is able to satisfy individual information
demands. This ranges from specific investigations of particular biological
entities like genes, the search for particular traits or metabolic functions
towards broad scans of available genomic knowledge to taxonomy or affiliation.
To evaluate LAILAPS’s relevance ranking with regard to the mentioned classes of
information demands a set of 20 IR query use cases was selected (see Table 2)
and ranked by a domain expert.
Afterwards, from each query result elements where randomly selected and
classified into five relevance classes: “fully agree”, ”minor quality doubts”,
“could be of relevance”, “undecided”, and “no relevance”. The result of this
evaluation is a set of 400 relevance ranked data base entries (M. Esch, Jinbo
Chen and Matthias Lange: Training dataset of LAILAPS ranking system
doi:10.5447/IPK/2014/2).
The evaluation results show that the LAILAPS ranking system separate well
between non-relevance results and relevance results but weak for the classes
„very good“ and „good” (see Figure 3). In comparison to term frequency – inverse
document frequency (TF/IDF) based scoring, which is one of the most prominently
used relevance scoring function in life science information systems, LAILAPS
show increased discriminating performance.
Regarding to the amount of expected query results, LAILAPS covered resources and
information retrieval potential is similar to established systems like UniProt
and NCBI GQuery. For the chosen query terms UniProt ranks results in a similar
way and top ranked results in LAILAPS can be found in top ranked proteins from
UniProt. Counting only the number of results can be misleading in respect of the
potential information density. Results include frequently replicated data e.g.
TrEMBL records of computational predicted annotation are excerpts of primary
text of UniProt etc. LAILAPS design principle is “less is more”. GQuery shows as
first result page an overview of the hit number in NCBI indexed databases but
does not merge them all together in one ranking system that the user needs to
decide first which special data source is the most interesting.
We compare LAILAPS depending on the requirement for a good and user friendly
information system in life science with other much used information systems. …
The results are summarized in Table 3.


Import of new data and update policy!

Material and Methods (~455/500):
LAILAPS uses different data sources which are indexed or linked to the system.
Several important plant genomic databases including protein sequence or ontology
information are indexed. The big advantage is the usage of linked data. All
information is stored in the original data source and makes an elaborate storage
and indexing not necessary.  All together 12 indexed and 13 linked plant
databases are used to search for information in. They are summarized in Table 1.
LAILAPS consists of a client and a server. On client side the user sends a
request which is received and processed by the server. The results are delivered
back to the client where the user will see a list of documents and linked genes.
Different technologies and algorithms are used in the backend to process the
request and analyze the data. To provide a quick document request a part of the
data is located in H2 (http://www.h2database.com) and another part is stored in
Oracle Berkeleys DB (http://www.oracle.com/us/products/database/berkeley-db). H2
is used for more complex SQL requests and Oracle for key query requests. Indexed
is the data using Apache Lucene to allow a fast information search. Calculating
the relevance of a document different mechanisms from the field of machine
learning are used to train data instead of using strict implemented
instructions. Database entries need other ranking algorithms than full text data
like it is stored in PubMed to recognize all given information. Therefore a
special feature model was created in LAILAPS (Lange et al. 2010) which recognize
features like attribute, database, keyword, frequency and co-occurrence of a
query, organism, sequence length, text position and synonyms. A list of all
features including a description is shown in Table 3.
The features get ranked by an artificial neural network (a machine learning
method). The ranking results and all influencing features for each document can
be looked up by the user. In Figure 4 an example ranking result is shown for the
query “flowering time in barley” and the document Q32QD1. As seen there is a
high co-occurrence for the request and found term parts in this document. The
found protein entry is coming from UniProt which causes a high database value as
well. It is also shown that there are much more indirect links to this document
instead of direct links. In this way every document is ranked in relation to
other ones.
To allow an indirect mapping to other data sources without indexing them,
identification mapping files are necessary which link specific gene ID’s of a
corresponding databases to metadata from indexed data. This metadata can be
various InterPro or PFam domains but also Gene Ontologies (GO) and other data.
In Fehler! Verweisquelle konnte nicht gefunden werden. the principle is
illustrated by an example. UniProt data such as the Protein Q09268 is saved and
indexed in the LAILAPS backend system. These documents refer to different
protein domains or GO’s which get mapped by a provided mapping file with the
respective ID’s of the linked database. If a mapping between the indexed and
linked data found, the gene ID is shown as an indirect link for the specific
document. In that way all indirect links are created. The mapping files are
provided by the linked database.

Funding:
This work was made in the frame of the transPLANT project and is funded by the
European Commission within its 7th Framework Programme, contract number 283496.

Acknowledgments:


Disclosures:
No conflicts of interest declared.

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